Measuring intramuscular fat

ABSTRACT

Dual-energy absorptiometry is used to estimate intramuscular adipose tissue metrics and display results, preferably as related to normative data. The process involves deriving x-ray measurements for respective pixel positions related to a two-dimensional projection image of a body slice containing intramuscular adipose tissue as well as subcutaneous adipose tissue, at least some of the measurements being dual-energy x-ray measurements, processing the measurements to derive estimates of metrics related to the intramuscular adipose tissue in the slice, and using the resulting estimates. Processing the measurements includes an algorithm which places boundaries of regions, e.g., a large region and a smaller region. The regions are combined in an equation that is highly correlated with intramuscular adipose tissue measured by quantitative computed tomography in order to estimate intramuscular adipose tissue.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 13/045,966, titled VISCERAL FAT MEASUREMENT, filed Mar. 11,2011, which is a continuation-in-part of U.S. patent application Ser.No. 12/730,051, titled ESTIMATING VISCERAL FAT BY DUAL-ENERGY X-RAYABSORPTIOMETRY, filed Mar. 23, 2010, both of which are incorporated byreference.

BACKGROUND OF THE INVENTION

The condition associated with loss of skeletal muscle mass and strengththat occurs with advancing age is known as Sarcopenia. Studies haveshown that an increase of intramuscular adipose tissue occurs in peoplewho suffer from the Sarcopenic condition. Consequently, measurement ofintramuscular adipose tissue may be useful for diagnostic purposes. Itis known in the art that intramuscular adipose tissue can be measured orestimated by differentiating it from subcutaneous adipose tissue (SAT)and muscle in thigh cross-sections or slices using computerizedtomography (CT) and magnetic resonance imaging (MRI). For example, anarea of SAT can be manually deselected from an image and an imagethresholding algorithm can then be used to distinguish muscle fromintramuscular adipose tissue. However, the relatively high cost of bothexaminations and the high radiation dosage of CT can discourage the useof these techniques as a screening tool for intramuscular adipose tissuelevels. Further, thresholding algorithms lack specificity because areasor volumes above the threshold can have different amounts of fatcontent, and areas or volumes below the threshold may not be fat-free.Thus, systematic errors can be introduced by assumptions of % fat inareas or volumes above or below the threshold.

SUMMARY OF THE INVENTION

In accordance with one non-limiting aspect of the invention a methodcomprises acquiring x-ray measurements for respective pixel positionsrelated to a two-dimensional projection image of a portion of a subject,wherein at least some of the measurements are dual-energy x-raymeasurements; placing a plurality of regions of the image; computerprocessing to combine the plurality of regions to provide an estimate ofintramuscular adipose tissue; and providing and displaying selectedresults related to said estimate of intramuscular adipose tissue.

In accordance with another non-limiting aspect of the invention a methodcomprises: acquiring x-ray measurements for respective pixel positionsrelated to a two-dimensional projection image of a portion of asubject's limb, wherein at least some of the measurements aredual-energy x-ray measurements; placing a first region of the imagewhich extends from a first side of the limb to a second side of thelimb; placing a second region which extends across a muscle; computerprocessing the first and second regions to provide an estimate ofintramuscular adipose tissue; and providing and displaying selectedresults related to said estimate of intramuscular adipose tissue.

In accordance with another non-limiting aspect of the invention anapparatus comprises a data acquisition unit including a scanner thatacquires x-ray measurements for respective pixel positions related to atwo-dimensional projection image of a portion of a subject, wherein atleast some of the measurements are dual-energy x-ray measurements; amemory in which is placed a plurality of regions of the image; aprocessing unit that computer-processes the regions to provide anestimate of intramuscular adipose tissue; and a display unit thatprovides and displays selected results related to intramuscular adiposetissue of the subject.

In accordance with another non-limiting aspect of the invention anapparatus comprises: a data acquisition unit including a scanner thatacquires x-ray measurements for respective pixel positions related to atwo-dimensional projection image of a portion of a subject's limb,wherein at least some of the measurements are dual-energy x-raymeasurements; a memory in which is placed a first region of the imagewhich extends from a first side of the limb to a second side of thelimb, and a second region which extends across a muscle; a processingunit that computer-processes the first and second regions to provide anestimate of intramuscular adipose tissue; and a display unit thatprovides and displays selected results related to intramuscular adiposetissue of the subject.

Aspects of the present invention provide advantages over the prior art.Dual-energy x-ray absorptiometry (DXA) exams are widely available,rapid, relatively low dose, and much less costly than CT and MRI exams.Further, DXA is capable of measuring both global and regional fat massbecause, for tissue paths that are projected as pixels in the x-rayimage, a given dual-energy x-ray measurement pertains to a uniquecombination of fat and lean mass. Consequently, DXA measurement ofintramuscular adipose tissue could be a preferred diagnostic tool forSarcopenia and other conditions.

In various non-limiting alternatives one or more functions can beautomated or partially automated with computer processing. For example,the first region can be automatically placed by a software tool usingvarious anatomical landmarks and the position of an upper region ofinterest line for reference. Further, the software tool mayautomatically place the second region based on % Fat inflection.Further, measurements of total adipose tissue in a fixed thicknessregion across the entire width of the limb can be combined with ameasurement of the adipose tissue in the same thickness region of themuscle plus whatever subcutaneous fat is present above and below themuscle region using a linear equation that is correlated withintramuscular adipose tissue measured by quantitative computedtomography in order to estimate intramuscular adipose tissue.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a simplified and schematic cross-sectional elevationillustrating a fan-shaped distribution of x-rays in a DXA system inwhich the intramuscular adipose tissue analysis described herein can bepracticed.

FIG. 2 illustrates a PA projection image of a patient taken with a DXAsystem.

FIG. 3 illustrates a cross-sectional image of a limb slice.

FIG. 4 illustrates placement of regions.

FIG. 5 illustrates a % fat profile and inflection points used for regionplacement.

FIG. 6 is a block diagram of a DXA system useful for estimatingintramuscular adipose tissue.

FIG. 7 is a cross-sectional image of a limb slice which illustrates useof more than two regions.

DETAILED DESCRIPTION

Referring to FIG. 1, a DXA system 10 includes a patient table 12 havinga support surface 14 that can be considered horizontal and planar inthis simplified explanation and illustration which is not necessarilyaccurate in scale or geometry, and which is used here solely toillustrate and explain certain principles of operation. A human subject26 is supine on surface 14. The length of the patient is along ahorizontal longitudinal axis defined as the y-axis and the patient'sarms are spaced from each other along the x-axis. A C-arm 16 hasportions 16 a and 16 b extending below and above table 10, respectively,and is mounted in a suitable structure (not shown expressly) for movingat least parallel to the y-axis along the length of patient 26. Lowerportion 16 a of the C-arm carries an x-ray source 20 that can emitx-rays limited by an aperture 22 into a fan-shaped distribution 24conforming to a plane perpendicular to the y-axis. The energy range ofthe x-rays can be relatively wide, to allow for the known DXAdual-energy x-ray measurements, or can be filtered or generated in anarrower range to allow for single energy x-ray measurements. The x-raydistribution can be continuous within the angle thereof or can be madeup, or considered to be made up, of individual narrower beams. The x-raydistribution 24 can encompass the entire width of the patient asillustrated, or it can have a narrower angle so the entire patient canbe covered only by several passes along the y-axis and the x-raymeasurements from the several passes can be combined as is known in theart to simulate the use of a wider fan beam, as typical in currentcommercial DXA systems. Alternatively, a single, pencil-like beam ofx-rays can be used to scan selected regions of the patient's body, e.g.in a raster pattern. The x-rays impinge on x-ray detector 28, which cancomprise one or more linear arrays of individual x-ray elements 30, eachlinear array extending in the x-direction, or a continuous detectorwhere measurements for different positions along the detector can bedefined in some manner known in the art, or can be another form ofdetector of x-rays. C-arm 16 can move at least along the y-axis, or canbe maintained at any desired position along that axis. For any oneposition, or any one unit of incremental travel in the y-direction ofarm 16, detector 28 can produce one or several lines of raw x-ray data.Each line can correspond to a row of pixels in a resulting image, whichrow extends in a direction corresponding to the x-direction. Each linecorresponds to a particular position, or range of positions, of theC-arm in its movement along the y-axis and/or a particular lineardetector, and comprises a number of individual measurements, each for arespective detector element position in the line, i.e., representsattenuation that the x-rays have suffered in traveling from source 20 toa respective detector element position over a specified time interval. ADXA system takes a higher x-ray energy measurement H and a lower x-rayenergy measurement L from each detector element position, and carriesout initial processing known in the art to derive, from the raw x-raydata, a set of pixel values for a projection image. Each pixel valuecomprises a high energy value H and a low energy value L. This can beachieved by rapidly alternating the energy level of the x-rays fromsource 20 between a higher and a lower range of x-ray energies, forexample by rapidly rotating or otherwise moving a suitable filter in orout of the x-rays before they reach patient 26, or by controlling thex-ray tube output, and/or by using an x-ray detector 28 that candiscriminate between energy ranges to produce H and L measurements foreach pixel position, e.g. by having a low energy and a high energydetector element side-by-side or on top of each other for respectivepositions in the detector array. The H and L x-ray measurements for therespective pixel positions are computer-processed as known in the art toderive estimates of various parameters, including, if desired, bodycomposition (total mass, fat mass, and lean mass).

FIG. 2 illustrates a PA projection image taken with the DXA system.Pixel values are derived from x-ray measurements for at least one limbslice 200. For example, measurements may be taken of one or both thighs,biceps, or any combination thereof. The slice is taken along the z-xplane and has a thickness (w) in the y-direction. For example, severalhundred pixel values in the x-direction and a several pixel values inthe y-direction are derived from the raw x-ray data. Typically but notnecessarily, the limb slice thickness w along the y-direction is severalmm, e.g. 10-15 mm.

FIG. 3 illustrates an x-ray image of slice 200 (FIG. 2) parallel to az-x plane through the thigh of a patient taken with a CT system. Theimage shows a ring (non-circular) of subcutaneous adipose tissue (SAT)between the skin 300 and muscle wall 303. The image also shows regionsof intramuscular adipose tissue 302, muscle 314, and thigh bone 330.

Referring to FIGS. 2 through 4, in accordance with one embodiment of theinvention the percentage of intramuscular adipose tissue is estimatedwith a DXA system using an empirical technique. A region of interest(ROI) is placed on a DXA scan slice 200 to delineate various anatomicalregions in accordance with the instructions in the User's Guide for theHologic DXA scanner. After the ROI has been placed on the scan, a largeregion and a smaller region, both rectangular in shape and 4 scan lines(5 cm) high, are placed at the location of the subject's limb. In theillustrated example the large region is defined by boundaries 306, 308,310, 312, and extends completely across the limb from one side to theopposite side. The smaller region is defined by boundaries 304 301, 306,308, centered within the large region, and extends across the muscle.Both the large and small regions can be placed by the user based onvisual inspection of the image. However, in accordance with anembodiment of the invention the larger region is automatically placed bya software tool that is stored in non-transitory computer readablememory and run by processing hardware. For example, the software toolmay place the larger region using various anatomical landmarks and theposition of the upper ROI line for reference.

Referring to FIGS. 3 through 5, the software tool may also automaticallyplace the smaller region within the larger region. In one embodimentthis is accomplished with an algorithm which places boundaries based on% Fat inflection. The upper and lower boundaries 306, 308 of the smallerregion are superimposed over the larger region such that the upper andlower coordinates of both regions are identical. The left and rightboundaries 304, 301 of the smaller region are then placed by thealgorithm. In particular, the algorithm initially operates on percentfat profile data corresponding to a position inside the left and rightboundaries 310, 312 of the large region, e.g., at the point where thesubcutaneous fat layer ends, and proceeds by operating on datacorresponding to an adjacent set of pixels moving in toward the centerthe limb from the left and right sides. Measurement of % Fat initiallyincreases and then decreases as the x-ray beam begins to scan the areaof muscle 314. This inflection point, which is indicative of theoutermost extent of the muscle, is detected by the algorithm, e.g., byidentifying that the % Fat values of two consecutive pixels are lowerthan the preceding pixel. The smaller region boundaries 304, 301 are setat the inflection point.

Regardless of how the boundaries which define the regions are placed, alinear regression technique that accounts for SAT between the boundariesof the larger region is used to estimate intramuscular adipose tissue.The large region defined by boundaries 306, 308, 310, 312 provides ameasurement of total adipose tissue in a 5 cm wide region across theentire width of the subject's limb. The smaller region defined byboundaries 304, 301, 306, 308 provides a measurement of the adiposetissue in the same 5 cm wide region of the limb plus whateversubcutaneous fat is present above (at region 320) and below (at region322) the muscle region in the two dimensional DXA projection. Constantpercent fat values at the center of the plot in FIG. 5 indicate imagepixels where bone 330 (FIG. 3) is present and percent fat cannot bedirectly measured. However, techniques for estimating percent fat valuesfor the region where bone is present and percent fat cannot be directlymeasured are known. The measurement (limb adipose mass) of total adiposetissue in a 5 cm wide region across the entire width of the subject'slimb and the measurement (muscle region adipose mass) of the adiposetissue in the same 5 cm wide region of the limb plus whateversubcutaneous fat is present above and below the muscle region in the twodimensional DXA projection is combined in a linear equation that ishighly correlated with intramuscular adipose tissue measured byquantitative computed tomography in order to estimate intramuscularadipose tissue (IAT) as:

DXA IAT=J*muscle region adipose mass−K*(limb adipose mass−muscle regionadipose mass)+b,   Eq. 1

where J and K are constants that optimize the correlation between DXAIAT and intramuscular adipose tissue measured by computed tomography,and b is the intercept term of the linear equation. It should be notedthat the values of J, K and b are not necessarily that same for allsubjects. For example, values of J, K and b can be dependent upon age,gender, ethnicity, weight, height, body mass index, waist circumference,and other anthropomorphic variables.

Those skilled in the art will understand how to determine thoseconstants in view of this disclosure.

The results of the processes described above can be in various forms andcan be used for a variety of purposes. For example, displays ofnumerical values can be used in assessing the health, treatment options,or treatments of a patient by a health professional. As another example,such numerical values or estimates derived therefrom can be used asinputs to automated systems for similar assessment or for treatmentplanning As yet another example, parameters related to fat metrics canbe displayed and recorded or printed as a part of an otherwise typicalreport including x-ray images and other DXA-produced information for apatient.

Estimates of intramuscular adipose tissue derived as discussed above canbe shown in a variety of ways. They can be displayed alone, or incombination with known or expected ranges of comparable estimates forpopulations believed to be “normal” or “healthy,” which ranges can bematched to the estimates for a patient by some characteristic such asage, sex, and/or ethnicity. The normal or healthy ranges for suchcharacteristics can be obtained by retrospective analysis of alreadycompleted studies and/or from new studies to obtain the data. Anintramuscular adipose tissue metric for a patient can be compared withan intramuscular adipose tissue metric for the same patient taken at adifferent time to estimate the change and/or the rate of change, forexample to see if visceral fat parameters have improved or havedeteriorated over some period of time or in relation to some treatmentor regimen. Such changes also can be matched to expected or known orestimated ranges to see if the change or rate of change for a patient isstatistically significant as distinguished from a change within theprecision range of the estimate. The intramuscular adipose tissueestimates derived as discussed above, or metrics based on suchestimates, can be used in other ways as well. One non-limiting exampleis to produce reports similar to those produced for BMD (bone mineraldensity) in current commercial bone densitometry (DXA) systems but formetrics of intramuscular adipose tissue rather than BMD estimates.

FIG. 6 illustrates in block diagram form a DXA system carrying out theprocesses described above for estimating intramuscular adipose tissue.The system can be one of the current DXA systems offered commercially bythe assignee programmed to carry out the disclosed processes, usingprogramming that a person of ordinary skill in the art can apply to aparticular commercially available DXA system without undueexperimentation, given the teachings in this patent specification. Thesystem includes a scanner 60, computer processing unit 62, userinterface 66, and a results presentation unit 64. The scanner mayinclude an x-ray source and x-ray detector. Scanner 60 also includesappropriate other components known in the art, such as power and controlunits, and operates to generate dual energy or single energy x-raymeasurements of the selected region or slice of a patient's body. Thecomputer processing unit 62 includes processing hardware andnon-transitory computer readable memory for controlling scanner 60 andprocessing x-ray measurements obtained thereby in accordance with thetechniques described above under corresponding programming. A resultspresentation unit 64 displays, prints, stores, and/or sends for furtherprocessing or storage, results such as in the form of images and/orcurves and/or numeric results indicative of intramuscular adipose tissueor % IAT, or some other parameter related to intramuscular fat or otherparameter discussed above, including in the immediately precedingparagraph. Units 62 and 64 communicate interactively with a user inputunit 66. The actual physical arrangement of system components may differfrom the functional illustration in FIG. 6.

FIG. 7 is a cross-sectional image of a body slice which illustrates analternative embodiment utilizing more than two regions. A large regiondefined by boundaries 706, 708, 710, 712 provides a measurement of totaladipose tissue in a 5 cm wide region across the entire width of thelimb. A smaller region which includes a first portion defined byboundaries 700, 705, 706, 708 and a second portion defined by boundaries702, 701, 706, 708 provides a measurement of the adipose tissue in thesame 5 cm wide region of the muscle area, exclusive of the bone 330region, and plus whatever subcutaneous fat is present above (at region320) and below (at region 322) the muscle region in the two dimensionalDXA projection. The “bone” region defined by boundaries 700, 706, 702,708 provides a measurement of adipose tissue where bone is present andpercent fat cannot be directly measured. A generalized linear equationfor combining the measurements of adipose tissue in order to estimateintramuscular adipose tissue (IAT) with three regions can be representedas:

DXA IAT=J*Regionl+K* Region2+L*Region3+b,  Eq. 2

where J, K and L are constants (which may differ from those of Eq. 1)that optimize the correlation between DXA IAT and intramuscular adiposetissue measured by computed tomography, and b is the intercept term ofthe linear equation. As in the previously described embodiment, thevalues of the constants (here J, K, and L) and intercept b are notnecessarily that same for all subjects. For example, values of J, K, Land b can be dependent upon age, gender, ethnicity, weight, height, bodymass index, waist circumference, and other anthropomorphic variables.Those skilled in the art will understand how to determine thoseconstants in view of this disclosure. Furthermore, the two region andthree region embodiments are merely exemplary, and any number of regionscould be defined and utilized to estimate IAT.

In an alternative embodiment polynomial expansion is used to estimateintramuscular adipose tissue. A generalized equation for combining themeasurements of adipose tissue using polynomial expansion in order toestimate intramuscular adipose tissue (IAT) can be represented as:

DXA IAT=J1(Region1)+J2(Region1)²+J3(Region1)³+ . . . ,  Eq. 3

where Jn and constants associated with the polynomial expansion of theother regions (eg. K_(n) and L_(n)) optimize the correlation between DXAIAT and intramuscular adipose tissue measured by computed tomography. Asin the previously described embodiment, the values of the constants arenot necessarily the same for all subjects, and can be dependent uponage, gender, ethnicity, weight, height, body mass index, waistcircumference, and other anthropomorphic variables.

The disclosure above is mainly in terms of SAT and intramuscular adiposetissue of human patients, but it should be clear that the approach isapplicable in other fields as well, such as in analysis of othersubjects, such as live animals and carcasses. Finally, while a currentlypreferred embodiment has been described in detail above, it should beclear that a variation that may be currently known or later developed orlater made possible by advances in technology also is within the scopeof the appended claims and is contemplated by and within the spirit ofthe detailed disclosure.

1. A method comprising: acquiring x-ray measurements for respectivepixel positions related to a two-dimensional projection image of aportion of a subject, wherein at least some of the measurements aredual-energy x-ray measurements; placing a plurality of regions of theimage; computer processing to combine the plurality of regions toprovide an estimate of intramuscular adipose tissue; and providing anddisplaying selected results related to said estimate of intramuscularadipose tissue.
 2. The method of claim 1 including combining theplurality of regions in a linear equation using constants that providecorrelation between dual-energy x-ray measured intramuscular adiposetissue and intramuscular adipose tissue measured by computed tomography.3. The method of claim 1 including combining the plurality of regionsusing polynomial expansion.
 4. The method of claim 1 including placing afirst region of the image which extends from a first side of a limb to asecond side of the limb, and placing a second region which extendsacross a muscle area from a first side to a second side betweenoutermost extents of muscle wall.
 5. The method of claim 1 includingplacing a first region of the image which extends from a first side of alimb to a second side of the limb, placing a second region which extendsacross a muscle area from a first side to a second side betweenoutermost extents of muscle wall but exclusive of a third region whichis placed where bone is present and percent fat cannot be directlymeasured.
 6. The method of claim 1 including computer processing atleast some of the x-ray measurements for placing at least one region ofthe image.
 7. The method of claim 4 including using an anatomicallandmark and a preselected region of interest line for placing the firstregion of the image.
 8. The method of claim 4 including computerprocessing at least some of the x-ray measurements for placing thesecond region of the image.
 9. The method of claim 8 includingidentifying a left and a right muscle wall by identifying inflection ofadipose tissue values for placing the second region of the image. 10.The method of claim 4 including combining the first region and thesecond region in a linear equation that is correlated with intramuscularadipose tissue measured by quantitative computed tomography forprocessing the first and second regions to provide an estimate ofintramuscular adipose tissue.
 11. The method of claim 10 includingcalculating intramuscular adipose tissue as: J*muscle region adiposemass−K*(limb adipose mass−muscle region adipose mass)+b.
 12. The methodof claim 11 including selecting constants J and K that providecorrelation between DXA intramuscular adipose tissue and intramuscularadipose tissue measured by computed tomography, and wherein b is anintercept term.
 13. The method of claim 11 including selecting a valuefor at least one of J, K and b for the subject.
 14. The method of claim13 including selecting a value for at least one of J, K and b based onat least one of age, gender, ethnicity, weight, height, body mass index,waist circumference, and other anthropomorphic variables of the subject.15. Apparatus comprising: a data acquisition unit including a scannerthat acquires x-ray measurements for respective pixel positions relatedto a two-dimensional projection image of a portion of a subject's limb,wherein at least some of the measurements are dual-energy x-raymeasurements; a memory in which is placed a plurality of regions of theimage; a processing unit that computer-processes the regions to providean estimate of intramuscular adipose tissue; and a display unit thatprovides and displays selected results related to intramuscular adiposetissue of the subject.
 16. The apparatus of claim 15 wherein theprocessing unit combines the plurality of regions in a linear equationusing constants that provide correlation between DXA intramuscularadipose tissue and intramuscular adipose tissue measured by computedtomography.
 17. The apparatus of claim 15 wherein the processing unitcombines the plurality of regions using polynomial expansion.
 18. Theapparatus of claim 15 wherein a first region and a second region arestored in memory, the first region extending from a first side of thelimb to a second side of the limb, and the second region extendingacross a muscle area from the first side to the second side betweenoutermost extents of muscle wall.
 19. The apparatus of claim 15 whereina first region, a second region, and a third region are stored inmemory, the first region extending from a first side of the limb to asecond side of the limb, the second region extending across a musclearea from a first side to a second side between outermost extents ofmuscle wall but exclusive of a third region which is placed where boneis present and percent fat cannot be directly measured.
 20. Theapparatus of claim 18 wherein the processing unit places the firstregion of the image by computer-processing at least some of the x-raymeasurements.
 21. The apparatus of claim 20 wherein the processing unituses an anatomical landmark and a preselected region of interest line toplace the first region of the image.
 22. The apparatus of claim 18wherein the processing unit places the second region of the image bycomputer-processing at least some of the x-ray measurements.
 23. Theapparatus of claim 22 wherein the processing unit uses an algorithm toidentify a left and a right outermost extent of muscle wall byidentifying inflection of adipose tissue values for placing the secondregion of the image.
 24. The apparatus of claim 18 wherein theprocessing unit combines the first region and the second region using alinear equation which is correlated with intramuscular adipose tissuemeasured by quantitative computed tomography.
 25. The apparatus of claim24 wherein the processing unit calculates intramuscular adipose tissueas: J*muscle region adipose mass−K*(limb adipose mass−muscle regionadipose mass)+b.
 26. The apparatus of claim 25 wherein constants J and Kare selected based on correlation between DXA intramuscular adiposetissue and intramuscular adipose tissue measured by computed tomography,and wherein b is an intercept term.
 27. The apparatus of claim 25wherein at least one of J, K and b are selected for the subject.
 28. Theapparatus of claim 27 wherein at least one of J, K and b are selectedbased on at least one of age, gender, ethnicity, weight, height, bodymass index, waist circumference, and other anthropomorphic variables ofthe subject.